



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType


_snake_case						=    logging.get_logger(__name__)

_snake_case						=    {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}



class     UpperCAmelCase_       (			a):
							lowerCamelCase__       =						'layoutlmv3'







							def __init__(  self,     __a=5_0265,     __a=768,     __a=12,     __a=12,     __a=3072,     __a="gelu",     __a=0.1,     __a=0.1,     __a=512,     __a=2,     __a=0.02,     __a=1E-5,     __a=1,     __a=0,     __a=2,     __a=1024,     __a=128,     __a=128,     __a=True,     __a=32,     __a=128,     __a=64,     __a=256,     __a=True,     __a=True,     __a=True,     __a=224,     __a=3,     __a=16,     __a=None,     **__a,     ):

														'''simple docstring'''

														super().__init__(
														    vocab_size=__a,     hidden_size=__a,     num_hidden_layers=__a,     num_attention_heads=__a,     intermediate_size=__a,     hidden_act=__a,     hidden_dropout_prob=__a,     attention_probs_dropout_prob=__a,     max_position_embeddings=__a,     type_vocab_size=__a,     initializer_range=__a,     layer_norm_eps=__a,     pad_token_id=__a,     bos_token_id=__a,     eos_token_id=__a,     **__a,     )
														_lowerCAmelCase	:				List[str]					  =       max_ad_position_embeddings
														_lowerCAmelCase	:				Any					  =       coordinate_size
														_lowerCAmelCase	:				Any					  =       shape_size
														_lowerCAmelCase	:				List[Any]					  =       has_relative_attention_bias
														_lowerCAmelCase	:				Tuple					  =       rel_pos_bins
														_lowerCAmelCase	:				List[Any]					  =       max_rel_pos
														_lowerCAmelCase	:				Any					  =       has_spatial_attention_bias
														_lowerCAmelCase	:				Dict					  =       rel_ad_pos_bins
														_lowerCAmelCase	:				Tuple					  =       max_rel_ad_pos
														_lowerCAmelCase	:				str					  =       text_embed
														_lowerCAmelCase	:				Tuple					  =       visual_embed
														_lowerCAmelCase	:				int					  =       input_size
														_lowerCAmelCase	:				List[str]					  =       num_channels
														_lowerCAmelCase	:				str					  =       patch_size
														_lowerCAmelCase	:				List[str]					  =       classifier_dropout



class     UpperCAmelCase_       (			a):
							lowerCamelCase__       =						version.parse('1.12')







							@property
							def   snake_case__ (  self):

														'''simple docstring'''

														if self.task in ["question-answering", "sequence-classification"]:
																					return OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "sequence"}),
																					        ("attention_mask", {0: "batch", 1: "sequence"}),
																					        ("bbox", {0: "batch", 1: "sequence"}),
																					        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
																					    ])
														else:
																					return OrderedDict(
																					    [
																					        ("input_ids", {0: "batch", 1: "sequence"}),
																					        ("bbox", {0: "batch", 1: "sequence"}),
																					        ("attention_mask", {0: "batch", 1: "sequence"}),
																					        ("pixel_values", {0: "batch", 1: "num_channels"}),
																					    ])







							@property
							def   snake_case__ (  self):

														'''simple docstring'''

														return 1E-5







							@property
							def   snake_case__ (  self):

														'''simple docstring'''

														return 12







							def   snake_case__ (  self,     __a,     __a = -1,     __a = -1,     __a = False,     __a = None,     __a = 3,     __a = 40,     __a = 40,     ):

														'''simple docstring'''

														setattr(processor.image_processor,     "apply_ocr",     __a)

														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														_lowerCAmelCase	:				Tuple					  =       compute_effective_axis_dimension(
														    __a,     fixed_dimension=OnnxConfig.default_fixed_batch,     num_token_to_add=0)
														# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
														_lowerCAmelCase	:				List[str]					  =       processor.tokenizer.num_special_tokens_to_add(__a)
														_lowerCAmelCase	:				Optional[Any]					  =       compute_effective_axis_dimension(
														    __a,     fixed_dimension=OnnxConfig.default_fixed_sequence,     num_token_to_add=__a)
														# Generate dummy inputs according to compute batch and sequence
														_lowerCAmelCase	:				Union[str, Any]					  =       [[" ".join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

														# Generate dummy bounding boxes
														_lowerCAmelCase	:				Optional[int]					  =       [[[48, 84, 73, 128]]] * batch_size

														# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
														# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
														_lowerCAmelCase	:				Dict					  =       self._generate_dummy_images(__a,     __a,     __a,     __a)

														_lowerCAmelCase	:				Optional[Any]					  =       dict(
														    processor(
														        __a,     text=__a,     boxes=__a,     return_tensors=__a,     ))

														return inputs






'''simple docstring'''


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_					=   Lock()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple					,       a_						:				Dict					,       a_						:				Optional[Any]					,       a_						:				str					,       a_						:				Any					,       a_						:				Optional[Any]      ):
  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0					,       10      ):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(a_      )
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      __a								=       rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      __a								=       min(a_					,       a_      )
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(a_      )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      __a								=       lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      __a								=       max(a_					,       a_      )
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       []
  __a								=       []
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()      )
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  __a								=       Pipe()
  __a								=       Pipe()
  process_array_.append(
      Process(
          target=a_					,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,       )      )
  __a								=       temp_rs
  __a								=       temp_rr

  for i in range(1					,       len(a_      ) - 1      ):
    __a								=       Pipe()
    __a								=       Pipe()
    process_array_.append(
        Process(
            target=a_					,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,       )      )
    __a								=       temp_rs
    __a								=       temp_rr

  process_array_.append(
      Process(
          target=a_					,       args=(
              len(a_      ) - 1,
              arr[len(a_      ) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(a_      ) - 1],
          )					,       )      )

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0					,       len(a_      )      ):
    __a								=       result_pipe[p][0].recv()
    process_array_[p].join()
  return arr


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       list(range(10					,       0					,       -1      )      )
  print('Initial List'      )
  print(*a_      )
  __a								=       odd_even_transposition(a_      )
  print('Sorted List\n'      )
  print(*a_      )


if __name__ == "__main__":
  main()



